使用步态,双重任务和机器学习来检测脑血管疾病中的认知障碍
Vânia Guimarães1,2, Inês Sousa3, Miguel Velhote Correia4,5
1Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, Porto, 4200-135, Portugal. vania.guimaraes@aicos.fraunhofer.pt.
BMC medical informatics and decision making
|April 1, 2025
概括
步行分析,包括双重任务成本,有效地检测中风后的认知障碍. 这种方法显示出作为替代或补充传统认知评估的承诺,如蒙特利尔认知评估 (MoCA).
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
背景情况:
- 认知障碍是中风的常见,但往往未被发现的后果.
- 早期检测对于及时干预和管理中风后认知缺陷至关重要.
研究的目的:
- 调查步态分析的有效性,结合双重任务的挑战,识别中风幸存者的认知障碍.
- 将步态指标的诊断性能与蒙特利尔认知评估 (MoCA) 进行比较.
主要方法:
- 分析了47名参与者的步行和神经心理数据 (29名受损,18名正常).
- 机器学习模型,包括支持矢量机器 (SVM),使用嵌套交叉验证进行训练.
- 步行指标,双重任务成本 (减去一个) 和MoCA分数被评估为分类性能.
主要成果:
- 一套包括双重任务成本在内的综合步行指标,使用SVM实现了高分类性能 (96.6%的灵敏度,61.1%的特异性).
- 蒙特利尔认知评估 (MoCA) 显示,与步态指标相比,分类性能较低.
- 将MoCA和步态指标结合起来,使特异性提高到72.2%,同时保持高灵敏度 (93.1%).
结论:
- 步态分析,特别是双重任务分析,是检测脑血管疾病患者认知障碍的宝贵工具.
- 步行指标可以作为一种有效的替代品或补充MoCA用于认知障碍查.
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